Automated image analysis of iris biometrics

Abstract

The iris of the eye is considered to be the most discriminatory of facial features. It is often assumed that an individual’s iris pattern remains unchanged throughout their lifetime. However, clinical findings suggest that changes in the iris can occur due to ageing and in response to external factors such as medications, disease and surgery. Such changes in the appearance of the iris need to be addressed when considering the iris as a biometric. The extent of change over time and whether this affects the appearance of the iris sufficiently to impact on its value as a biometric measure requires investigation. To enable a detailed study of iris stability, a novel database of high resolution iris images is presented comprising 364 irides with images captured at regular time intervals. To facilitate iris analysis, an enhanced localisation method is proposed for iris segmentation. An extensive evaluation of a number of feature extraction algorithms is described and applied to a database of iris images captured over increasing time intervals of three, six, nine and twelve months. Irides are analysed in order to determine whether significant variation exists between images captured at increasing time lapses and to assess the impact of such variations on recognition performance. Iris matching schemes are also evaluated and compared. Results are presented in which increased dissimilarity is observed in iris comparisons as the time interval between comparisons increases. These differences increase the probability of recognition failure in iris recognition. A greater number of recognition failures are observed as the time lapse between images increases. Such failures are also found to differ depending on iris texture pattern. It is concluded that whilst the iris remains an appropriate biometric identifier, it may not be as stable as originally proposed. Further research is required to determine the causes of the observed longitudinal variation in iris pattern and the possible impact on iris recognition systems.

Thesis is embargoed until 28th February 2014

Date of Award	May 2012
Original language	English
Sponsors	Department of Education and Learning (DEL)
Supervisor	Bryan Scotney (Supervisor), Philip Morrow (Supervisor) & BARBARA PIERSCIONEK (Supervisor)